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Course Outline

Introduction to Agent Builder and RAG

  • Exploring the capabilities of Agent Builder
  • Core principles of RAG and appropriate application scenarios
  • Real-world use cases and success stories

Environment Configuration

  • Setting up the Vertex AI workspace
  • Linking search and vector storage systems
  • Practical lab: Preparing the working environment

Designing Grounded Agent Workflows

  • Establishing agent objectives and dialogue flows
  • Aligning data sources with retrieval strategies
  • Practical lab: Constructing a conversation flow

Implementing RAG Pipelines

  • Indexing documents and managing embeddings
  • Utilizing retriever and re-ranker patterns
  • Practical lab: Building a RAG pipeline

Integrations and Enterprise Data

  • Establishing secure connections to internal systems
  • Managing data governance and access permissions
  • Practical lab: Linking enterprise data sources

Testing, Evaluation, and Iteration

  • Conducting prompt testing and measuring performance metrics
  • Employing user simulation and validation techniques
  • Practical lab: Assessing and optimizing agent behavior

Deployment, Monitoring, and Maintenance

  • Reviewing deployment options and scalability factors
  • Tracking performance, relevance, and drift over time
  • Developing operational guides for updates and rollback procedures

Summary and Future Directions

Requirements

  • Fundamental understanding of natural language processing
  • Practical experience with cloud services and APIs
  • Knowledge of search mechanisms and vector databases

Target Audience

  • Software Developers
  • Solution Architects
  • Product Managers
 14 Hours

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